🔍demand forecasting

Historical pattern

Demand forecasting

ETS model decomposition: 

## # A fable: 6 x 6 [1M]
## # Key:     description, material_type, .model [1]
##   description material_type         .model     date movement_out_quantity  .mean
##   <chr>       <chr>                 <chr>     <mth>                <dist>  <dbl>
## 1 EXAMPLE 1   own manufactured pro… ETS(m… 2024 Mar     N(16040, 9.3e+07) 16040.
## 2 EXAMPLE 1   own manufactured pro… ETS(m… 2024 Apr     N(16040, 1.9e+08) 16040.
## 3 EXAMPLE 1   own manufactured pro… ETS(m… 2024 May     N(16040, 2.8e+08) 16040.
## 4 EXAMPLE 1   own manufactured pro… ETS(m… 2024 Jun     N(16040, 3.7e+08) 16040.
## 5 EXAMPLE 1   own manufactured pro… ETS(m… 2024 Jul     N(16040, 4.7e+08) 16040.
## 6 EXAMPLE 1   own manufactured pro… ETS(m… 2024 Aug     N(16040, 5.6e+08) 16040.

ARIMA model decomposition

## Series: movement_out_quantity 
## Model: ARIMA(1,0,0) 
## 
## Coefficients:
##          ar1
##       0.9235
## s.e.  0.0687
## 
## sigma^2 estimated as 85959117:  log likelihood=-127.08
## AIC=258.16   AICc=259.49   BIC=259.13